Effect of Alert Presentation Mode and Hazard Direction on Driver Takeover from an Autonomous Vehicle
Bibliographic record
Abstract
Autonomous vehicles are becoming increasingly common. Although the level of automation varies between vehicles even the most advanced occasionally require driver input when the driving situation is complex, or the quality of the sensory data is poor. If driver input is needed the system must alert drivers that they will have to take over but these alerts may vary in their effectiveness in prompting rapid driver takeover (time to grip the steering wheel, percentage of appropriate takeover maneuvers) and situational awareness (driver attention to the threat that necessitated take over and understanding for why take over is necessary). In this study, we used a driving simulator operating in autonomous mode to compare 2 alert types (audio-visual, and audio alone) in 3 different takeover scenarios where hazards emerged from the front (a construction zone) or the left or right side (erratic behaviour in another driver: a rogue vehicle heading toward the drivers’ lane). We found that the takeover-time was faster after the audio-visual alert than the audio alert and situation awareness was better. The nature and direction of the hazard also had an effect. Situation awareness was poorer for hazards in front of the vehicle (a looming construction zone) as compared to the left and right of the driver (rogue vehicles heading toward the driver). These findings have important implications for interface design in autonomous vehicles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".